| Year |
Citation |
Score |
| 2021 |
Endo D, Kobayashi R, Bartolo R, Averbeck BB, Sugase-Miyamoto Y, Hayashi K, Kawano K, Richmond BJ, Shinomoto S. A convolutional neural network for estimating synaptic connectivity from spike trains. Scientific Reports. 11: 12087. PMID 34103546 DOI: 10.1038/s41598-021-91244-w |
0.33 |
|
| 2019 |
Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S. Reconstructing neuronal circuitry from parallel spike trains. Nature Communications. 10: 4468. PMID 31578320 DOI: 10.1038/S41467-019-12225-2 |
0.464 |
|
| 2018 |
Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, ... ... Shinomoto S, et al. Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214. PMID 30976604 DOI: 10.1146/annurev-statistics-041715-033733 |
0.703 |
|
| 2018 |
Fujita K, Medvedev A, Koyama S, Lambiotte R, Shinomoto S. Identifying exogenous and endogenous activity in social media Physical Review E. 98. DOI: 10.1103/Physreve.98.052304 |
0.312 |
|
| 2016 |
Onaga T, Shinomoto S. Emergence of event cascades in inhomogeneous networks. Scientific Reports. 6: 33321. PMID 27625183 DOI: 10.1038/Srep33321 |
0.325 |
|
| 2016 |
Mochizuki Y, Onaga T, Shimazaki H, Shimokawa T, Tsubo Y, Kimura R, Saiki A, Sakai Y, Isomura Y, Fujisawa S, Shibata K, Hirai D, Furuta T, Kaneko T, Takahashi S, ... ... Shinomoto S, et al. Similarity in Neuronal Firing Regimes across Mammalian Species. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 36: 5736-47. PMID 27225764 DOI: 10.1523/Jneurosci.0230-16.2016 |
0.721 |
|
| 2016 |
Kostal L, Shinomoto S. Efficient information transfer by Poisson neurons. Mathematical Biosciences and Engineering : Mbe. 13: 509-20. PMID 27106184 DOI: 10.3934/Mbe.2016004 |
0.412 |
|
| 2015 |
Yamanaka Y, Amari S, Shinomoto S. Microscopic instability in recurrent neural networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 91: 032921. PMID 25871186 DOI: 10.1103/Physreve.91.032921 |
0.52 |
|
| 2014 |
Mochizuki Y, Shinomoto S. Analog and digital codes in the brain. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 022705. PMID 25353507 DOI: 10.1103/Physreve.89.022705 |
0.383 |
|
| 2014 |
Kim H, Shinomoto S. Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation. Mathematical Biosciences and Engineering : Mbe. 11: 49-62. PMID 24245682 DOI: 10.3934/Mbe.2014.11.49 |
0.46 |
|
| 2013 |
Shinomoto S, Kim H. Estimating inputs and an internal neuronal parameter from a single spike train. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2013: 7096-9. PMID 24111380 DOI: 10.1109/EMBC.2013.6611193 |
0.366 |
|
| 2013 |
Koyama S, Omi T, Kass RE, Shinomoto S. Information transmission using non-poisson regular firing. Neural Computation. 25: 854-76. PMID 23339613 DOI: 10.1162/Neco_A_00420 |
0.37 |
|
| 2013 |
Mochizuki Y, Shinomoto S. Difference in modes of firing rate modulation between cortical areas Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P359 |
0.492 |
|
| 2012 |
Kim H, Shinomoto S. Estimating nonstationary input signals from a single neuronal spike train. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 86: 051903. PMID 23214810 DOI: 10.1103/Physreve.86.051903 |
0.477 |
|
| 2012 |
Shintani T, Shinomoto S. Detection limit for rate fluctuations in inhomogeneous Poisson processes. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 85: 041139. PMID 22680450 DOI: 10.1103/Physreve.85.041139 |
0.334 |
|
| 2012 |
Kim H, Richmond BJ, Shinomoto S. Neurons as ideal change-point detectors. Journal of Computational Neuroscience. 32: 137-46. PMID 21643776 DOI: 10.1007/S10827-011-0344-X |
0.446 |
|
| 2011 |
Yamauchi S, Kim H, Shinomoto S. Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times. Frontiers in Computational Neuroscience. 5: 42. PMID 22203798 DOI: 10.3389/Fncom.2011.00042 |
0.479 |
|
| 2011 |
Kobayashi R, Shinomoto S, Lansky P. Estimation of time-dependent input from neuronal membrane potential. Neural Computation. 23: 3070-93. PMID 21919789 DOI: 10.1162/Neco_A_00205 |
0.43 |
|
| 2011 |
Omi T, Shinomoto S. Optimizing time histograms for non-Poissonian spike trains. Neural Computation. 23: 3125-44. PMID 21919781 DOI: 10.1162/Neco_A_00213 |
0.468 |
|
| 2011 |
Shinomoto S, Omi T, Mita A, Mushiake H, Shima K, Matsuzaka Y, Tanji J. Deciphering elapsed time and predicting action timing from neuronal population signals. Frontiers in Computational Neuroscience. 5: 29. PMID 21734877 DOI: 10.3389/Fncom.2011.00029 |
0.461 |
|
| 2011 |
Omi T, Kanter I, Shinomoto S. Optimal observation time window for forecasting the next earthquake. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 83: 026101. PMID 21405883 DOI: 10.1103/Physreve.83.026101 |
0.304 |
|
| 2010 |
Shinomoto S. Fitting a stochastic spiking model to neuronal current injection data. Neural Networks : the Official Journal of the International Neural Network Society. 23: 764-9. PMID 20478693 DOI: 10.1016/J.Neunet.2010.04.004 |
0.481 |
|
| 2010 |
Shimokawa T, Koyama S, Shinomoto S. A characterization of the time-rescaled gamma process as a model for spike trains. Journal of Computational Neuroscience. 29: 183-91. PMID 19844786 DOI: 10.1007/S10827-009-0194-Y |
0.363 |
|
| 2010 |
Shimazaki H, Shinomoto S. Kernel bandwidth optimization in spike rate estimation. Journal of Computational Neuroscience. 29: 171-82. PMID 19655238 DOI: 10.1007/S10827-009-0180-4 |
0.559 |
|
| 2010 |
Shinomoto S, Shimazaki H, Shimokawa T. Characterizing neuronal firing with the rate and the irregularity Neuroscience Research. 68: e50-e51. DOI: 10.1016/J.Neures.2010.07.471 |
0.452 |
|
| 2009 |
Kobayashi R, Tsubo Y, Shinomoto S. Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers in Computational Neuroscience. 3: 9. PMID 19668702 DOI: 10.3389/Neuro.10.009.2009 |
0.484 |
|
| 2009 |
Shinomoto S, Kim H, Shimokawa T, Matsuno N, Funahashi S, Shima K, Fujita I, Tamura H, Doi T, Kawano K, Inaba N, Fukushima K, Kurkin S, Kurata K, Taira M, et al. Relating neuronal firing patterns to functional differentiation of cerebral cortex. Plos Computational Biology. 5: e1000433. PMID 19593378 DOI: 10.1371/Journal.Pcbi.1000433 |
0.414 |
|
| 2009 |
Shimokawa T, Shinomoto S. Estimating instantaneous irregularity of neuronal firing. Neural Computation. 21: 1931-51. PMID 19323639 DOI: 10.1162/Neco.2009.08-08-841 |
0.51 |
|
| 2009 |
Kobayashi R, Tshubo Y, Shinomoto S. A fast-computational spiking neuron model adaptable to any cortical neuron Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P22 |
0.469 |
|
| 2009 |
Shimazaki H, Shinomoto S. Histogram binwidth and kernel bandwidth selection for the spike-rate estimation Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P116 |
0.526 |
|
| 2009 |
Shinomoto S, Kim H, Shimokawa T. Neuronal firing patterns and cerebral cortical functions Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P106 |
0.38 |
|
| 2009 |
Shimokawa T, Shinomoto S. Bayesian estimation of the time-varing rate and irregularity of neuronal firing Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-O6 |
0.495 |
|
| 2009 |
Kim H, Richmond BJ, Shinomoto S. Detecting a change point by a single neuron Neuroscience Research. 65: S133. DOI: 10.1016/J.Neures.2009.09.652 |
0.382 |
|
| 2009 |
Kobayashi R, Tsubo Y, Shinomoto S. A simple model for predicting spike times of a variety of neurons Neuroscience Research. 65: S65. DOI: 10.1016/J.Neures.2009.09.197 |
0.448 |
|
| 2008 |
Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W. A benchmark test for a quantitative assessment of simple neuron models. Journal of Neuroscience Methods. 169: 417-24. PMID 18160135 DOI: 10.1016/J.Jneumeth.2007.11.006 |
0.429 |
|
| 2007 |
Omi T, Shinomoto S. Reverberating activity in a neural network with distributed signal transmission delays. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 76: 051908. PMID 18233688 DOI: 10.1103/Physreve.76.051908 |
0.343 |
|
| 2007 |
Shinomoto S, Koyama S. A solution to the controversy between rate and temporal coding. Statistics in Medicine. 26: 4032-8. PMID 17525932 DOI: 10.1002/Sim.2932 |
0.345 |
|
| 2007 |
Shimazaki H, Shinomoto S. A method for selecting the bin size of a time histogram. Neural Computation. 19: 1503-27. PMID 17444758 DOI: 10.1162/Neco.2007.19.6.1503 |
0.581 |
|
| 2007 |
Kobayashi R, Shinomoto S. State space method for predicting the spike times of a neuron. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 75: 011925. PMID 17358202 DOI: 10.1103/Physreve.75.011925 |
0.453 |
|
| 2007 |
Inaba N, Shinomoto S, Yamane S, Takemura A, Kawano K. MST neurons code for visual motion in space independent of pursuit eye movements. Journal of Neurophysiology. 97: 3473-83. PMID 17329625 DOI: 10.1152/Jn.01054.2006 |
0.329 |
|
| 2007 |
Koyama S, Shinomoto S. Inference of intrinsic spiking irregularity based on the Kullback-Leibler information. Bio Systems. 89: 69-73. PMID 17321039 DOI: 10.1016/J.Biosystems.2006.05.012 |
0.372 |
|
| 2007 |
Koyama S, Shimokawa T, Shinomoto S. Phase transitions in the estimation of event rate: A path integral analysis Journal of Physics a: Mathematical and Theoretical. 40: F383-F390. DOI: 10.1088/1751-8113/40/20/F01 |
0.309 |
|
| 2007 |
Shimazaki H, Shinomoto S. A recipe for optimizing a time-histogram Advances in Neural Information Processing Systems. 1289-1296. |
0.418 |
|
| 2007 |
Kobayashi R, Shinomoto S. Predicting spike times from subthreshold dynamics of a neuron Advances in Neural Information Processing Systems. 721-728. |
0.325 |
|
| 2006 |
Shimokawa T, Shinomoto S. Inhibitory neurons can facilitate rhythmic activity in a neural network. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 73: 066221. PMID 16906960 DOI: 10.1103/Physreve.73.066221 |
0.445 |
|
| 2005 |
Shinomoto S, Miyazaki Y, Tamura H, Fujita I. Regional and laminar differences in in vivo firing patterns of primate cortical neurons. Journal of Neurophysiology. 94: 567-75. PMID 15758054 DOI: 10.1152/Jn.00896.2004 |
0.457 |
|
| 2005 |
Shinomoto S, Miura K, Koyama S. A measure of local variation of inter-spike intervals. Bio Systems. 79: 67-72. PMID 15649590 DOI: 10.1016/J.Biosystems.2004.09.023 |
0.342 |
|
| 2005 |
Kobayashi R, Miyazaki Y, Shinomoto S. Faithful and unfaithful students in time series learning Ima Journal of Applied Mathematics (Institute of Mathematics and Its Applications). 70: 657-665. DOI: 10.1093/Imamat/Hxh090 |
0.349 |
|
| 2005 |
Koyama S, Shinomoto S. Empirical Bayes interpretations of random point events Journal of Physics a: Mathematical and General. 38: L531-L537. DOI: 10.1088/0305-4470/38/29/L04 |
0.387 |
|
| 2004 |
Tsubo Y, Kaneko T, Shinomoto S. Predicting spike timings of current-injected neurons. Neural Networks : the Official Journal of the International Neural Network Society. 17: 165-73. PMID 15036335 DOI: 10.1016/J.Neunet.2003.11.005 |
0.422 |
|
| 2004 |
Koyama S, Shinomoto S. Histogram bin width selection for time-dependent Poisson processes Journal of Physics a: Mathematical and General. 37: 7255-7265. DOI: 10.1088/0305-4470/37/29/006 |
0.338 |
|
| 2003 |
Shinomoto S, Shima K, Tanji J. Differences in spiking patterns among cortical neurons. Neural Computation. 15: 2823-42. PMID 14629869 DOI: 10.1162/089976603322518759 |
0.473 |
|
| 2003 |
Miyazaki Y, Kinzel W, Shinomoto S. Learning of time series through neuron-to-neuron instruction Journal of Physics a: Mathematical and General. 36: 1315-1322. DOI: 10.1088/0305-4470/36/5/309 |
0.377 |
|
| 2002 |
Shinomoto S, Sakai Y, Ohno H. Recording site dependence of the neuronal spiking statistics. Bio Systems. 67: 259-63. PMID 12459306 DOI: 10.1016/S0303-2647(02)00083-7 |
0.446 |
|
| 2002 |
Shinomoto S, Shima K, Tanji J. New classification scheme of cortical sites with the neuronal spiking characteristics. Neural Networks : the Official Journal of the International Neural Network Society. 15: 1165-9. PMID 12425435 DOI: 10.1016/S0893-6080(02)00093-X |
0.374 |
|
| 2001 |
Shinomoto S, Tsubo Y. Modeling spiking behavior of neurons with time-dependent Poisson processes. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 64: 041910. PMID 11690055 DOI: 10.1103/Physreve.64.041910 |
0.445 |
|
| 1999 |
Sakai Y, Funahashi S, Shinomoto S. Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. Neural Networks : the Official Journal of the International Neural Network Society. 12: 1181-1190. PMID 12662653 DOI: 10.1016/S0893-6080(99)00053-2 |
0.432 |
|
| 1999 |
Shinomoto S, Sakai Y, Funahashi S. The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Computation. 11: 935-51. PMID 10226190 DOI: 10.1162/089976699300016511 |
0.479 |
|
| 1999 |
Shinomoto S, Sakai Y. Inter-spike interval statistics of cortical neurons Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1606: 171-179. DOI: 10.1007/BFb0098171 |
0.366 |
|
| 1998 |
Shinomoto S, Sakai Y. Spiking mechanisms of cortical neurons Philosophical Magazine B: Physics of Condensed Matter; Statistical Mechanics, Electronic, Optical and Magnetic Properties. 77: 1549-1555. DOI: 10.1080/13642819808205047 |
0.46 |
|
| 1992 |
Amari S, Fujita N, Shinomoto S. Four Types of Learning Curves Neural Computation. 4: 605-618. DOI: 10.1162/Neco.1992.4.4.605 |
0.435 |
|
| 1990 |
Shinomoto S. Information classification scheme of feedforward networks organised under unsupervised learning Network: Computation in Neural Systems. 1: 135-147. DOI: 10.1088/0954-898X_1_2_002 |
0.41 |
|
| 1987 |
Shinomoto S. A cognitive and associative memory. Biological Cybernetics. 57: 197-206. PMID 3676357 DOI: 10.1007/Bf00364151 |
0.361 |
|
| 1970 |
Kobayashi R, Tsubo Y, Shinomoto S. A fast-computational spiking neuron model for a variety of cortical neuron Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.088 |
0.468 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2009 |
Shimokawa T, Shinomoto S. Bayesian estimation of nonstationary parameters of a single spike train Neuroscience Research. 65: S134. DOI: 10.1016/J.Neures.2009.09.659 |
0.297 |
|
| 2014 |
Onaga T, Shinomoto S. Bursting transition in a linear self-exciting point process. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 042817. PMID 24827303 DOI: 10.1103/Physreve.89.042817 |
0.288 |
|
| 1997 |
Aihara T, Tsukada M, Crair MC, Shinomoto S. Stimulus-dependent induction of long-term potentiation in CA1 area of the hippocampus: experiment and model. Hippocampus. 7: 416-26. PMID 9287081 DOI: 10.1002/(Sici)1098-1063(1997)7:4<416::Aid-Hipo7>3.0.Co;2-G |
0.287 |
|
| 2017 |
Furukawa M, Shinomoto S. Inferring objects from a multitude of oscillations Neural Computing and Applications. 30: 2471-2478. DOI: 10.1007/S00521-016-2752-3 |
0.285 |
|
| 1986 |
Shinomoto S. Statistical Properties of Neural Networks Progress of Theoretical Physics. 75: 1313-1318. DOI: 10.1143/Ptp.75.1313 |
0.276 |
|
| 2010 |
Zhao X, Omi T, Matsuno N, Shinomoto S. A non-universal aspect in the temporal occurrence of earthquakes New Journal of Physics. 12. DOI: 10.1088/1367-2630/12/6/063010 |
0.271 |
|
| 2010 |
Kobayashi R, Shinomoto S, Lansky P. A Method to Estimate Synaptic Input from a Voltage Trace Neuroscience Research. 68: e441. DOI: 10.1016/J.Neures.2010.07.1954 |
0.262 |
|
| 1987 |
Sakaguchi H, Shinomoto S, Kuramoto Y. Local and Grobal Self-Entrainments in Oscillator Lattices Progress of Theoretical Physics. 77: 1005-1010. DOI: 10.1143/Ptp.77.1005 |
0.262 |
|
| 2008 |
Omi T, Shinomoto S. Can distributed delays perfectly stabilize dynamical networks? Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 77: 046214. PMID 18517717 DOI: 10.1103/Physreve.77.046214 |
0.262 |
|
| 1986 |
Shinomoto S, Kuramoto Y. Cooperative Phenomena in Two-Dimensional Active Rotator Systems Progress of Theoretical Physics. 75: 1319-1327. DOI: 10.1143/Ptp.75.1319 |
0.261 |
|
| 2017 |
Fujita K, Shinomoto S, Rocha LEC. Correlations and forecast of death tolls in the Syrian conflict. Scientific Reports. 7: 15737. PMID 29146926 DOI: 10.1038/S41598-017-15945-X |
0.259 |
|
| 2020 |
Hoang H, Sato MA, Shinomoto S, Tsutsumi S, Hashizume M, Ishikawa T, Kano M, Ikegaya Y, Kitamura K, Kawato M, Toyama K. Improved hyperacuity estimation of spike timing from calcium imaging. Scientific Reports. 10: 17844. PMID 33082425 DOI: 10.1038/s41598-020-74672-y |
0.258 |
|
| 2016 |
Kostal L, Shinomoto S. Efficient information transfer by poisson neurons Mathematical Biosciences and Engineering. 13: 509-520. DOI: 10.3934/mbe.2016004 |
0.256 |
|
| 1991 |
Shinomoto S, Kabashima Y. Finite time scaling of energy in simulated annealing Journal of Physics a: Mathematical and General. 24: L141-L144. DOI: 10.1088/0305-4470/24/3/008 |
0.246 |
|
| 1988 |
Sakaguchi H, Shinomoto S, Kuramoto Y. Phase Transitions and Their Bifurcation Analysis in a Large Population of Active Rotators with Mean-Field Coupling Progress of Theoretical Physics. 79: 600-607. DOI: 10.1143/Ptp.79.600 |
0.245 |
|
| 1987 |
Shinomoto S. Memory maintenance in neural networks Journal of Physics a: Mathematical and General. 20: L1305-L1309. DOI: 10.1088/0305-4470/20/18/015 |
0.244 |
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| 1995 |
Kabashima Y, Shinomoto S. Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries Neural Computation. 7: 158-172. DOI: 10.1162/Neco.1995.7.1.158 |
0.243 |
|
| 1991 |
Kabashima Y, Shinomoto S. Asymptotic dependence of the residual energy on annealing time Journal of the Physical Society of Japan. 60: 3993-3996. DOI: 10.1143/Jpsj.60.3993 |
0.241 |
|
| 1992 |
Kabashima Y, Shinomoto S. Learning Curves for Error Minimum and Maximum Likelihood Algorithms Neural Computation. 4: 712-719. DOI: 10.1162/Neco.1992.4.5.712 |
0.239 |
|
| 2010 |
Yamauchi S, Kim H, Shinomoto S. Dynamical behavior of multi-timescale adaptive threshold model Neuroscience Research. 68: e434. DOI: 10.1016/J.Neures.2010.07.1924 |
0.237 |
|
| 2011 |
Kobayashi R, Tsubo Y, Lansky P, Shinomoto S. Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.227 |
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| 1988 |
Sakaguchi H, Shinomoto S, Kuramoto Y. Mutual Entrainment in Oscillator Lattices with Nonvariational Type Interaction Progress of Theoretical Physics. 79: 1069-1079. DOI: 10.1143/Ptp.79.1069 |
0.219 |
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| 1985 |
Kuramoto Y, Shinomoto S. Random Frequency Modulation of a Forced Nonlinear Oscillator Progress of Theoretical Physics. 73: 638-648. DOI: 10.1143/Ptp.73.638 |
0.215 |
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| 1986 |
Shinomoto S, Kuramoto Y. Phase Transitions in Active Rotator Systems Progress of Theoretical Physics. 75: 1105-1110. DOI: 10.1143/Ptp.75.1105 |
0.211 |
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| 2021 |
Koyama S, Horie T, Shinomoto S. Estimating the time-varying reproduction number of COVID-19 with a state-space method. Plos Computational Biology. 17: e1008679. PMID 33513137 DOI: 10.1371/journal.pcbi.1008679 |
0.155 |
|
| 2015 |
Onaga T, Shinomoto S. Bursting activity spreading through asymmetric interactions Proceedings - 10th International Conference On Signal-Image Technology and Internet-Based Systems, Sitis 2014. 388-395. DOI: 10.1109/SITIS.2014.66 |
0.074 |
|
| 2022 |
Shinomoto S, Tsubo Y, Marunaka Y. Detection and categorization of severe cardiac disorders based solely on heart period measurements. Scientific Reports. 12: 17019. PMID 36221030 DOI: 10.1038/s41598-022-21260-x |
0.073 |
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| 1982 |
Shinomoto S. Elementary theory of the equation of state and the pair distribution function for the hard sphere system Physics Letters A. 89: 19-22. DOI: 10.1016/0375-9601(82)90136-0 |
0.062 |
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| 1983 |
Shinomoto S. Hole Model Approach to Melting: Hard Sphere System Progress of Theoretical Physics. 70: 687-696. DOI: 10.1143/PTP.70.687 |
0.061 |
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| 1984 |
Shinomoto S, Morita T. Application of the cluster variation method to the hole theory of fluids Physica a: Statistical Mechanics and Its Applications. 127: 141-151. DOI: 10.1016/0378-4371(84)90124-9 |
0.061 |
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| 2006 |
Kuramoto Y, Shinomoto S, Nakao H, Ohta T. Progress of Theoretical Physics Supplement: Preface Progress of Theoretical Physics Supplement. 161: i. |
0.04 |
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| 1993 |
Kabashima Y, Shinomoto S. Acceleration of learning in binary choice problems . 446-452. |
0.038 |
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| 1983 |
Shinomoto S. Equilibrium theory for the hard-core systems Journal of Statistical Physics. 32: 105-113. DOI: 10.1007/BF01009423 |
0.036 |
|
| 1984 |
Shinomoto S. Note on the Hole Theory of Liquids: Free Volume of the Hard Particle Systems and Its Relation to Melting Progress of Theoretical Physics. 71: 1129-1141. DOI: 10.1143/PTP.71.1129 |
0.033 |
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| 1993 |
Kabashima Y, Shinomoto S. Incremental learning with and without queries in binary choice problems Proceedings of the International Joint Conference On Neural Networks. 2: 1637-1640. |
0.03 |
|
| 2002 |
Konoike T, Matsumura K, Yorifuji T, Shinomoto S, Ide Y, Ohya T. Practical enantioselective synthesis of endothelin antagonist S-1255 by dynamic resolution of 4-methoxychromene-3-carboxylic acid intermediate. The Journal of Organic Chemistry. 67: 7741-9. PMID 12398498 DOI: 10.1021/jo0261092 |
0.027 |
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| 1984 |
Shinomoto S, Morita T. FREE VOLUME OF THE HOLE THEORY OF FLUIDS Technology Reports of the Tohoku University. 49: 103-114. |
0.024 |
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| 1982 |
Shinomoto S. Direct proof of the H-theorem for the (two-body) Bogolubov-Green-Cohen equation Physica a: Statistical Mechanics and Its Applications. 112: 466-478. DOI: 10.1016/0378-4371(82)90188-1 |
0.021 |
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| Hide low-probability matches. |